Expression of transmembrane protein 26 (TMEM26) in breast cancer and its association with drug response

Identification and Validation of Immune-Related Long Non-Coding RNA Signature for Predicting Immunotherapeutic Response and Prognosis in NSCLC Patients Treated With Immunotherapy

Background

Numerous studies have reported that long non-coding RNAs (lncRNAs) play important roles in immune-related pathways in cancer. However, immune-related lncRNAs and their roles in predicting immunotherapeutic response and prognosis of non-small cell lung cancer (NSCLC) patients treated with immunotherapy remain largely unexplored.

Methods

Transcriptomic data from NSCLC patients were used to identify novel lncRNAs by a custom pipeline. ImmuCellAI was utilized to calculate the infiltration score of immune cells. The marker genes of immunotherapeutic response-related (ITR)-immune cells were used to identify immune-related (IR)-lncRNAs. A co-expression network was constructed to determine their functions. LASSO and multivariate Cox analyses were performed on the training set to construct an immunotherapeutic response and immune-related (ITIR)-lncRNA signature for predicting the immunotherapeutic response and prognosis of NSCLC. Four independent datasets involving NSCLC and melanoma patients were used to validate the ITIR-lncRNA signature.

Results

In total, 7,693 novel lncRNAs were identified for NSCLC. By comparing responders with non-responders, 154 ITR-lncRNAs were identified. Based on the correlation between the marker genes of ITR-immune cells and lncRNAs, 39 ITIR-lncRNAs were identified. A co-expression network was constructed and the potential functions of 38 ITIR-lncRNAs were annotated, most of which were related to immune/inflammatory-related pathways. Single-cell RNA-seq analysis was performed to confirm the functional prediction results of an ITIR-lncRNA, LINC01272. Four-ITIR-lncRNA signature was identified and verified for predicting the immunotherapeutic response and prognosis of NSCLC. Compared with non-responders, responders had a lower risk score in both NSCLC datasets (P<0.05). NSCLC patients in the high-risk group had significantly shorter PFS/OS time than those in the low-risk group in the training and testing sets (P<0.05). The AUC value was 1 of responsiveness in the training set. In melanoma validation datasets, patients in the high-risk group also had significantly shorter OS/PFS time than those in the low-risk group (P<0.05). The ITIR-lncRNA signature was an independent prognostic factor (P<0.001).

Conclusion

Thousands of novel lncRNAs in NSCLC were identified and characterized. In total, 39 ITIR-lncRNAs were identified, 38 of which were functionally annotated. Four ITIR-lncRNAs were identified as a novel ITIR-lncRNA signature for predicting the immunotherapeutic response and prognosis in NSCLC patients treated with immunotherapy.

The tamoxifen-regulated, long non-coding RNA LINC00992 affects proliferation, migration, and expression of tamoxifen resistance-associated genes in MCF-7 breast cancer cells

Introduction

Tamoxifen-adapted MCF-7 breast cancer cells (MCF-7-TAM-R) are a model for acquired tamoxifen resistance in oestrogen receptor-positive breast cancer. In this system, the expression of long-non-coding RNA LINC00992 is decreased. LINC00992 might therefore contribute to tamoxifen adaption and associated gene expres-sion changes. Here, we investigated whether a modulation of LINC00992 modifies gene expression, proliferation, and migration.

Material and methods

Up- and down-- regulation of LINC00992 was performed using plasmid vectors and siRNA. Gene expression was measured via nCounter® and quantitative real-time polymerase chain reaction. Database analysis was performed using GEPIA2 and cBioportal. Furthermore, we performed scratch assays, colony-forming assays, and proliferation assays with MCF-7 and MCF-7-TAM-R after up-regulation of LINC00992.

Results

Up- and down-regulation of LINC00992 caused gene expression changes in 4 of the 42 tamoxifen-regulated genes tested. Especially ubiquitin D, single-minded homologue 1 (SIM1) carcinoembryonic antigen-related cell adhesion molecule 5 and the G-protein coupled oestrogen receptor 1 were affected. In tamoxifen-adapted MCF-7-TAM-R cells, LINC00992 overexpression resulted in augmented viability and proliferation and enhanced migration. Database analyses revealed that luminal breast cancers have increased expression of LINC00992 compared to Her2-type/neu- or basal type. Furthermore, higher expression of LINC00992 was associated with poor prognosis in luminal-A carcinomas.

Conclusions

Changes in the expression of tamoxifen-regulated genes could be induced by manipulating LINC00992's abundance, suggesting that it is at least partially involved in the establishment of the tamoxifen-induced gene expression pattern. LINC00992 may also serve as a prognostic biomarker and may indicate the development of tamoxifen resistance.

Protein‑ and growth‑modulatory effects of carcinoma‑associated fibroblasts on breast cancer cells: Role of interleukin‑6

Carcinoma-associated fibroblasts (CAFs) secrete factors that increase the expression and/or activities of proteins in breast cancer cells and induce resistance to anti-estrogens, such as fulvestrant. A major factor is interleukin-6 (IL-6). This study demonstrated that, across estrogen receptor (ER) α-positive and -negative cell lines, recombinant human IL-6 (rhIL-6) mimicked most of the CAF-conditioned medium (CM)-induced changes in protein expression patterns; however, in most cases, it failed to recapitulate CAF-CM-triggered alterations in ERK1/2 and AKT activities. The ability of rhIL-6 to induce fulvestrant resistance was dependent upon the culture conditions. In 3D, but not in 2D cultures, rhIL-6 increased the survival of fulvestrant-treated cells, although not to the same extent as observed with CAF-CM. In 2D cultures, rhIL-6 acted in a pro-apoptotic manner and decreased the expression of ATP-binding cassette transporter G2 (ABCG2). The inhibition of the PI3K/AKT pathway had similar effects on apoptosis and ABCG2 expression, linking the failure of rhIL-6 to induce fulvestrant resistance to its inability to activate the PI3K/AKT pathway. In 3D cultures, both CAF-CM and rhIL-6 acted in an anti-apoptotic manner. These activities are likely independent on the PI3K/AKT pathway and ABCG2. Experiments on ERα-negative breast cancer cells revealed a growth-inhibitory effects of both CAF-CM and rhIL-6, which coincided with a reduction in the c-Myc level. These data suggest that IL-6 plays a role in several effects of CAF-CM, including alterations in protein expression patterns, fulvestrant resistance in 3D cultures and growth inhibition. By contrast, IL-6 is unlikely to be responsible for the CAF-CM-induced activation of the PI3K/AKT pathway and fulvestrant resistance in 2D cultures.

Genome-Wide Association Study of H/L Traits in Chicken

Presently, the heterophil-to-lymphocyte (H/L) ratio is being studied extensively as a disease resistance trait. Through intricate mechanisms to identify and destroy pathogenic microorganisms, heterophils play a pivotal role in the immune defense systems of avian species. To reveal the genetic basis and molecular mechanisms affecting the H/L ratio, phenotypic and H/L data from 1650 white feather chicken broilers were used in performing a genome-wide association study. A self-developed, chicken-specific 55K chip was used for heterophils, lymphocytes, and H/L classification, according to individual genomic DNA profiles. We identified five significant single nucleotide polymorphisms (SNPs) when the genome-wide significance threshold was set to 5% (p < 2.42 × 10^-6). A total of 15 SNPs obtained seemingly significant levels (p < 4.84 × 10^-5). Gene annotation indicated that CARD11 (Caspase recruitment domain family member 11), BRIX1 (Biogenesis of ribosomes BRX1), and BANP (BTG3 associated nuclear protein) play a role in H/L-associated cell regulation and potentially constitute candidate gene regions for cellular functions dependent on H/L ratios. These results lay the foundation for revealing the genetic basis of disease resistance and future marker-assisted selection for disease resistance.

Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas

BACKGROUND

The tumor-associated microenvironment plays important roles in tumor progression and drug resistance. However, systematic investigations of macrophage-tumor cell interactions to identify novel macrophage-related gene signatures in gliomas for predicting patient prognoses and responses to targeted therapies are lacking.

METHODS

We developed a multicellular gene network approach to investigating the prognostic role of macrophage-tumor cell interactions in tumor progression and drug resistance in gliomas. Multicellular gene networks connecting macrophages and tumor cells were constructed from re-grouped drug-sensitive and drug-resistant samples of RNA-seq data in mice gliomas treated with BLZ945 (a CSF1R inhibitor). Subsequently, a differential network-based COX regression model was built to identify the risk signature using a cohort of 310 glioma samples from the Chinese Glioma Genome Atlas database. A large independent validation set of 690 glioma samples from The Cancer Genome Atlas database was used to test the prognostic significance and accuracy of the gene signature in predicting prognosis and targeted therapeutic response of glioma patients.

RESULTS

A macrophage-related gene signature was developed consisting of twelve genes (ANPEP, DPP4, PRRG1, GPNMB, TMEM26, PXDN, CDH6, SCN3A, SEMA6B, CCDC37, FANCA, NETO2), which was tested in the independent validation set to examine its prognostic significance and accuracy. The generation of 1000 random gene signatures by a bootstrapping scheme justified the non-random nature of the macrophage-related gene signature. Moreover, the discovered gene signature was verified to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics and outperformed other existing gene signatures. Additionally, the macrophage-related gene signature was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures.

CONCLUSION

The multicellular gene network approach developed herein indicates profound roles of the macrophage-mediated tumor microenvironment in the progression and drug resistance of gliomas. The identified macrophage-related gene signature has good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients, implying that combining current targeted therapies with new macrophage-targeted therapy may be beneficial for the long-term treatment outcomes of glioma patients.

Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Long-term exposure to carcinoma-associated fibroblasts makes breast cancer cells addictive to integrin β1

We studied the long-term effect of stromal factors on the development of fulvestrant-resistance (FR) and fulvestrant-induced dormancy (D). Sublines established from stroma-treated FR-cells (C-FR cells) and D-cells (C-D cells) show permanently high expression of integrin β1 as well as Bcl-3 and P-STAT3 (C-FR) or IGF1R (C-D). Yet, cells fail to withstand fulvestrant better and do not migrate or grow faster than control cells. Instead, C-D cells rely on stromal factors to perform as well as control cells. In addition, C-FR cells adapted to integrin β1 for growth in 3D cultures. These data suggest that long-term exposure to stromal factors leads to addiction rather than better performance in cellular activities. We also found that morphologically distinct breast cancer cell line subpopulations share key responses to stromal factors suggesting that intratumoral heterogeneity may play a minor role in the interaction between breast cancer and stromal cells.